Articles | Volume 3, issue 4
Research article
14 Dec 2015
Research article |  | 14 Dec 2015

Estimating the volume of Alpine glacial lakes

S. J. Cook and D. J. Quincey

Abstract. Supraglacial, moraine-dammed and ice-dammed lakes represent a potential glacial lake outburst flood (GLOF) threat to downstream communities in many mountain regions. This has motivated the development of empirical relationships to predict lake volume given a measurement of lake surface area obtained from satellite imagery. Such relationships are based on the notion that lake depth, area and volume scale predictably. We critically evaluate the performance of these existing empirical relationships by examining a global database of glacial lake depths, areas and volumes. Results show that lake area and depth are not always well correlated (r2 = 0.38) and that although lake volume and area are well correlated (r2 = 0.91), and indeed are auto-correlated, there are distinct outliers in the data set. These outliers represent situations where it may not be appropriate to apply existing empirical relationships to predict lake volume and include growing supraglacial lakes, glaciers that recede into basins with complex overdeepened morphologies or that have been deepened by intense erosion and lakes formed where glaciers advance across and block a main trunk valley. We use the compiled data set to develop a conceptual model of how the volumes of supraglacial ponds and lakes, moraine-dammed lakes and ice-dammed lakes should be expected to evolve with increasing area. Although a large amount of bathymetric data exist for moraine-dammed and ice-dammed lakes, we suggest that further measurements of growing supraglacial ponds and lakes are needed to better understand their development.

Short summary
We compiled data on Alpine glacial lake morphometry to test empirical relationships that are used to estimate lake volume for the modelling of glacial lake outburst floods. We find wide scatter in the relationship between lake area and depth, and between area and volume, and identify contexts where existing empirical relationships are poor volume predictors. We generate a data-driven conceptual model of how lake volume should be expected to scale with area for a range of glacial lake contexts.